ADMET Prediction Skill
Predict ADMET properties to prioritize compounds for development.
Quick Start
text /admet "CC1=CC=C(C=C1)CNC" --full
/pk-prediction --library compounds.sdf --threshold 0.7
/toxicity-screen CHEMBL210 --include hERG,DILI,Ames
What's Included
Property Prediction Model Absorption Caco-2, HIA, Pgp ML/QSAR Distribution VDss, PPB, BBB ML/QSAR Metabolism CYP inhibition, clearance ML/QSAR Excretion Clearance, half-life ML/QSAR Toxicity hERG, DILI, Ames, mutagenicity ML/QSAR
Output Structure
markdown # ADMET Profile: CHEMBL210 (Osimertinib)
## Summary
| Property | Value | Status |
|----------|-------|--------|
| Drug-likeness | Pass | ✓ |
| Lipinski Ro5 | 0 violations | ✓ |
| VEBER | Pass | ✓ |
| PAINS | 0 alerts | ✓ |
| Brenk | 0 alerts | ✓ |
## Absorption
| Property | Prediction | Confidence |
|----------|------------|-------------|
| HIA | 98% | High |
| Caco-2 | 15.2 × 10⁻⁶ cm/s | High |
| Pgp substrate | Yes | Medium |
| F30% | 65% | Medium |
## Distribution
| Property | Prediction | Confidence |
|----------|------------|-------------|
| VDss | 5.2 L/kg | Medium |
| PPB | 95% | High |
| BBB | Yes | High |
| CNS MPO | 5.5 | Good |
## Metabolism
| Property | Prediction | Confidence |
|----------|------------|-------------|
| CYP3A4 substrate | Yes | High |
| CYP3A4 inhibitor | Yes | Medium |
| CYP2D6 inhibitor | No | High |
| CYP2C9 inhibitor | No | Medium |
| Clearance | 8.5 mL/min/kg | Low |
## Excretion
| Property | Prediction | Confidence |
|----------|------------|-------------|
| Renal clearance | 10% | Medium |
| Half-life | 48 hours | High |
## Toxicity
| Property | Prediction | Confidence |
|----------|------------|-------------|
| hERG inhibition | No | High |
| DILI | Concern | Medium |
| Ames mutagenicity | Negative | High |
| Carcinogenicity | Negative | Medium |
| Respiratory toxicity | No | Low |
## Recommendations
**Strengths**:
- Good oral bioavailability (65%)
- Brain penetration (BBB permeable)
- Low hERG risk
**Concerns**:
- DILI concern - monitor in preclinical studies
- CYP3A4 inhibition - potential DDIs
**Overall**: Good ADMET profile. Progress to in vivo PK.
Property Ranges
Drug-Likeness
Rule Pass Criteria Lipinski Ro5 ≤ 1 violation Veber RotB ≤ 10, PSA ≤ 140 Ų Egan LogP ≤ 5, PSA ≤ 131 Ų MDDR MW ≤ 600, LogP ≤ 5
Absorption
Property Good Moderate Poor HIA >80% 40-80% <40% Caco-2 >10 1-10 <1 F30% >70% 30-70% <30%
Distribution
Property Good Moderate Poor VDss 0.3-5 L/kg <0.3 or >5 Extreme PPB <90% 90-95% >95% BBB LogBB > 0.3 -0.3 to 0.3 < -0.3
Toxicity Alerts
Alert Action hERG inhibition Cardiotoxicity risk DILI positive Hepatotoxicity risk Ames positive Mutagenicity risk PAINS Assay interference Structural alerts Investigate further
Running Scripts
bash # Full ADMET profile
python scripts/admet_predict.py --smiles "CC1=CC=C..." --full
# Batch prediction
python scripts/admet_predict.py --library compounds.sdf --output results.csv
# Specific properties
python scripts/admet_predict.py --smiles "..." --properties hERG,DILI,CYP
# Filter by criteria
python scripts/admet_filter.py --library compounds.sdf --rules lipinski,veber
Requirements
bash pip install rdkit
# Optional for advanced models
pip install deepchem admet-x
Reference
Best Practices
Use multiple models : Consensus predictions more reliableCheck confidence : Low confidence = experimental verification neededConsider chemistry : Novel structures less reliableIterative design : Use predictions to guide synthesisValidate early : Confirm key predictions experimentally
Common Pitfalls
Pitfall Solution Over-reliance on predictions Experimental validation required Ignoring confidence Check model applicability domain Single model only Use consensus of multiple models Ignoring chemistry Novel scaffolds = uncertain predictions Late-stage testing Early ADMET screening saves time
Limitations
Models are approximate : Errors commonNovel chemistry : Less reliable for new scaffoldsIn vitro-in vivo gap : Predictions don't always translateSpecies differences : Human predictions based on animal dataComplex mechanisms : Some toxicity not predicted 
